Welcome

About us…

Floris van Ogtrop - Unit Coordinator

  • Room 306, Level 3, Biomedical Building, Australian Technology Park, Eveleigh
  • Ph: 02 8627 1024
  • Email: floris.vanogtrop@sydney.edu.au

Teaching schedule

Januar Harianto
Weeks 1 – 4, Lecturer

Floris van Ogtrop
Weeks 5 – 8, Unit Coordinator

Si Yang Han
Weeks 9 – 12, Lecturer

ENVX1002 Learning outcomes

  • LO1. Implement basic reproducible research practices – including consistent data organisation, documented code, and version-controlled workflows so that statistical analyses and results can be readily replicated and validated by others.
  • LO2. Demonstrate proficiency in utilising R and Excel to effectively explore and describe life science datasets.
  • LO3. Apply parametric and non-parametric statistical inference methods to experimental and observational data using RStudio and effectively interpret and communicate the results in the context of the data.
  • LO4. Be able to put into practice both linear and non-linear models to describe relationships between variables using RStudio and Excel, demonstrating creativity in developing models that effectively represent complex data patterns.
  • LO5. Be able to articulate statistical and modelling results clearly and convincingly in both written reports and oral presentations, working effectively as an individual and collaboratively in a team, showcasing the ability to convey complex information to varied audiences.

Delivery format

All lectures and tutorials are held in ABS Lecture Theatre 1130. Lab sessions are held in the Biomedical Building, Australian Technology Park, Eveleigh.

  • Lectures (recorded): deliver content, provide context, and introduce new concepts
  • Tutorials (recorded): practice and apply concepts from lectures, prep for labs
  • Labs: hands-on practice with R and data analysis, with demonstrators to help you
  • Ed discussion: online forum for questions and discussions
  • Drop-in sessions: additional help and support as needed on Zoom or in person

Timetable

Lectures

  • Monday 12pm–1pm, ABS Lecture Theatre 1130
  • Tuesday 9am–10am, ABS Lecture Theatre 1130

Tutorials

  • Tuesday 10am–11am, ABS Lecture Theatre 1130
  • 1-hour tutorial directly following your lecture

Computer Labs

  • 2-hour in-person lab session with demonstrators
  • Biomedical Building, Australian Technology Park, Eveleigh
  • See timetable for your allocated time

Schedule at a glance…

Code 
sequenceDiagram
    participant M as Mon
    participant T as Tue
    participant W as Wed
    participant Th as Thu
    participant F as Fri
    participant S as Sat
    participant Su as Sun

    Note over M,T: Lectures (recorded) - ABS LT 1130
    Note over T: Tutorial (recorded) - ABS LT 1130
    Note over T,Th: Lab Sessions - Biomedical Building
    Th->>+Su: Self-revision, pick ONE day (encouraged)

sequenceDiagram
    participant M as Mon
    participant T as Tue
    participant W as Wed
    participant Th as Thu
    participant F as Fri
    participant S as Sat
    participant Su as Sun

    Note over M,T: Lectures (recorded) - ABS LT 1130
    Note over T: Tutorial (recorded) - ABS LT 1130
    Note over T,Th: Lab Sessions - Biomedical Building
    Th->>+Su: Self-revision, pick ONE day (encouraged)

Resources

Where are the Labs?

  • Lab sessions include extra time (30 minutes) for travel – already programmed in the timetable (so clashes are avoided)
  • A free shuttle service is available between campus and the labs, but the schedule is very limited
  • Take advantage of the new community access gates at Redfern Station: saves 5 minutes

Content & assessments

Topic outline

  • Week 01 - Data: Reproducible science
  • Week 02 - Data: Introduction to statistical programming
  • Week 03 - Data: Exploring and visualising data
  • Week 04 - Data: The Central Limit Theorem
  • Week 05 - Inference: 1-sample tests
  • Week 06 - Inference: 2-sample tests
  • Week 07 - Inference: Non-parametric tests 1
  • Week 08 - Inference: Non-parametric tests 2
  • Week 09 - Modelling: Describing relationships
  • Week 10 - Modelling: Linear functions
  • Week 11 - Modelling: Linear functions – multiple predictors
  • Week 12 - Modelling: Non-linear functions
  • Week 13 - Revision: Past exam questions and review

Assessments

Code 
# calculate this year's year number
library(lubridate)
year <- year(Sys.Date())
address <- paste0(
  "https://www.sydney.edu.au/units/ENVX1002/",
  year,
  "-S1C-ND-CC"
)

The most up to date (and slightly more comprehensive) information for 2025 is here. In a nutshell:

ENVX1002 Assessments
Week Assessment Description
3 Early Feedback Quiz (individual 5%) In-person - 15 minutes
5 Project 1: Exploring data (individual 15%) Written report, 500 words
8 Coding and data skills evaluation (individual 15%) In-person - 50 minutes
13 Project 2: Modelling (10% + Peer assessment 5%) Group presentation - 5 minutes
Exam Final exam (individual 50%) MCQ + SAQ Questions - 2 hours

Any questions?

Software and tools

Baby steps…

  • This unit is designed for beginners - no prior statistics or programming required
  • We start with basics – pace increases after week 4
  • Focus on understanding concepts first, then tools
  • We provide plenty of support – more on this later

Our tech stack

  1. MS Excel – for data entry and basic analysis
  2. R – a programming language for data analysis
  3. RStudio – an integrated development environment (IDE) for R
  4. Quarto (Markdown) – a key platform for reproducible reports and documents
  5. GitHub Copilot – AI-powered code completion tool. Optional

MS Excel

  • Widely used for data entry and basic analysis
  • Versatile and user-friendly – most of you are already familiar with it
  • Great data exploration tool

R




  • A free, open-source programming language
  • Widely used for data analysis and statistics
  • Standard tool in scientific research
  • Extensive collection of packages for data science
  • Strong support for creating publication-quality graphics
  • Large, active community for help and resources

Why R?

  1. Beginner friendly (works without needing to know a lot of programming)
  2. Makes your work reproducible
  3. Powerful yet accessible
  • Importantly – the skills you learn are highly transferable to other tools and languages such as Python and Java.
  • Most easily integrated with generative AI tools – more on this soon
  • Well-documented and discussed online (so you can find help easily)

RStudio

  • NOT the same as R – it’s an integrated development environment (IDE)
  • Runs R (…and Python, and SQL, and more)
  • Makes it easier to write and run R code by providing a significantly more user-friendly interface

Starting with R

  • It’s normal to feel overwhelmed at first
  • We’ll learn step by step
  • Practice is key - a little bit each day helps
  • Don’t hesitate to ask questions!

Satisfying when it works

Click to see the code for this animation 
# Load required packages
library(gapminder) # Dataset of country statistics over time
library(gganimate) # For creating animations in ggplot
library(tidyverse) # Collection of data science packages

# Create an animated plot showing how life expectancy relates to GDP
ggplot(
  gapminder,
  aes(gdpPercap, lifeExp,
    size = pop,
    colour = country
  )
) +
  geom_point(
    alpha = 0.7,
    show.legend = FALSE
  ) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  labs(
    title = "Year: {frame_time}",
    x = "GDP per capita (log scale)",
    y = "Life expectancy (years)",
    caption = "Data: Gapminder"
  ) +
  theme_minimal(base_size = 12) +
  transition_time(year) +
  ease_aes("linear")

Note: we will revisit this plot in the next lecture

Quarto

  • Majority of our resources are built using Quarto – a markdown-based document format that you will learn to use in this unit
    • Lecture slides
    • Tutorials
    • Lab exercises
  • Quarto makes everything reproducible - what does it mean?
  • Free and open source, available on the ENVX resources GitHub repository – re-use and modify as you wish (but follow CC BY 4.0)
## Quarto

- Majority of our resources are built using [**Quarto**](https://quarto.org/) -- 
a markdown-based document format that **you will learn to use** in this unit
  - Lecture slides
  - Tutorials
  - Lab exercises
- Quarto  makes everything **reproducible** - what does it mean?
- Free and open source, available on the [ENVX resources](https://github.com/ENVX-resources) 
GitHub repository -- re-use and modify as you wish 
(but follow [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))

R, RStudio, Quarto!?

  • Again, it’s normal to feel overwhelmed at first
  • These technologies are complementary – everything is implemented in RStudio
  • The tutorials and labs will guide you through the process

If you need help…

Seek help early

  • You are not alone, and you need to learn to ask for help
  • We provide a LOT of support in various forms:
    • Face-to-face (in a group setting): tutorials, labs
    • Face-to-face (one-on-one): consultations – book a time with us (email)
    • Online (collaborative): Ed for general questions
    • Online (private): use private posts on Ed
  • From time to time, we will organise drop-in sessions for additional help

NOTE: We cannot help you if you don’t ask!

Advice from 2024 ENVX1002 students!

  • “Practicals are important.”
  • “Ed discussion is your saviour (but check if someone has asked the same q before posting).”
  • “The lab notes are really helpful for the projects - so turn up and pay attention.”
  • “Start work on the assignments early so that if you need help, you can get it.”
  • “Complete the tutorials before the prac as it makes the prac a lot more meaningful”
  • “Watch the lectures.”
  • “Taking step by step”
  • “Keep track of the code and what it means.”
  • “Do not ignore the practicals and tutorials, they teach the most.”
  • “Don’t be closed minded.”
  • “work with others to make notes for codes.”
  • “find a stats buddy in your prac to talk things over with!”
  • “Ask questions, speak up if labs are moving too fast passed concepts and ask how things are relevant to out into practice.”
  • “The experience that teachers and demos have makes it far more relatable and helps cement an idea through inference.”
  • “Stick with it, focus in the pracs and all is fine.”
  • “Keep up with tutorial quizzes, code along with the tutorials as well as the practical, the more code you do, the more the content sticks in your head.”
  • “Show up to the labs and don’t rely on copilot or chat gpt.”
  • “Study after practicals to ensure you understand code.”
  • “do not panic and always ask for help, and do not stress too much over stats.”

Thanks!

Tomorrow: Lecture (1h) and then Tutorial (1h) – see you there!

This presentation is based on the SOLES Quarto reveal.js template and is licensed under a Creative Commons Attribution 4.0 International License.

References and Resources

Core Reading

  • Quinn & Keough (2002). Experimental Design and Data Analysis for Biologists. Cambridge University Press. Sections 1.1-1.2, pages 1-7.

Software Resources